Selectively doping barlowite for quantum spin liquid: A first-principles study
Bibliographic record
Abstract
Barlowite ${\mathrm{Cu}}_{4}{(\mathrm{OH})}_{6}\mathrm{FBr}$ is a newly found mineral containing ${\mathrm{Cu}}^{2+}$ kagome planes. Despite similarities in many aspects to herbertsmithite ${\mathrm{Cu}}_{3}\mathrm{Zn}{(\mathrm{OH})}_{6}{\mathrm{Cl}}_{2}$, the well-known quantum spin liquid (QSL) candidate, intrinsic barlowite turns out not to be a QSL, possibly due to the presence of ${\mathrm{Cu}}^{2+}$ ions in between kagome planes that induce interkagome magnetic interaction [Phys. Rev. Lett. 113, 227203 (2014)]. Using first-principles calculation, we systematically study the feasibility of selective substitution of the interkagome Cu ions with isovalent nonmagnetic ions as a function of ion concentration up to the stoichiometric limit. Unlike previous speculation of using larger dopants, such as ${\mathrm{Cd}}^{2+}$ and ${\mathrm{Ca}}^{2+}$, we identify the most ideal stoichiometric doping elements to be Mg and Zn in forming ${\mathrm{Cu}}_{3}\mathrm{Mg}{(\mathrm{OH})}_{6}\mathrm{FBr}$ and ${\mathrm{Cu}}_{3}\mathrm{Zn}{(\mathrm{OH})}_{6}\mathrm{FBr}$ with the highest site selectivity and smallest lattice distortion. The equilibirium antisite disorder in Mg/Zn-doped barlowite is estimated to be one order of magnitude lower than that in herbertsmithite. The single-electron band structure and orbital component analysis show that the proposed selective doping effectively mitigates the difference between barlowite and herbertsmithite.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".